assessing flood vulnerability index for policy

American Journal of Water Science and Engineering 2021; 7(2): 24-38 http://www.sciencepublishinggroup.com/j/ajwse doi: 10.11648/j.ajwse.20210702.11 ISSN: 2575-1867 (Print); ISSN: 2575-1875 (Online)

Assessing Flood Vulnerability Index for Policy Implications Towards Flood Risk Management Along the Atlantic Coast of Limbe, Cameroon

Usongo Patience Ajonina, Tepoule Nguéke Joseph, Chang Linda Meh

Department of Geography, University of Buea, Buea, Cameroon

Email address:

To cite this article: Usongo Patience Ajonina, Tepoule Nguéke Joseph, Chang Linda Meh. Assessing Flood Vulnerability Index for Policy Implications Towards

Flood Risk Management Along the Atlantic Coast of Limbe, Cameroon. American Journal of Water Science and Engineering.

Vol. 7, No. 2, 2021, pp. 24-38. doi: 10.11648/j.ajwse.20210702.11

Received: March 9, 2021; Accepted: March 22, 2021; Published: April 7, 2021

Abstract: Floods along the Atlantic coast of Limbe are the most predominant natural disaster posing serious threats to man and

the environment. Without adequate information about the risk levels and why the implementation of locally appropriate

adaptation measures are less effective, flood disasters will continue to become more rampant and disastrous. The ability to

accurately identify, measure and evaluate the various vulnerabilities of affected people and communities is a right step towards

reducing disaster risk. This article focuses on developing a Flood Vulnerability Index (FVI) based on exposure, susceptibility and

resilience factors that will guide putting in place specific adaptation plans targeted at reducing the impacts of floods. The study

made use of the mixed research design method. Reponses were gathered from 183 respondents using questionnaires and focus

group discussions (FGD) from household heads to construct an integrated vulnerability index made up of 22 indicators grouped

into susceptibility indicators (15), resilience (5) and exposure (2). A handheld Global Positioning System (GPS) was used in the

measurement of distance and elevation. Data collected was subjected to analysis of variance to test if significant differences in

vulnerability exist within the neighborhoods and the level of success of adaptation strategies was also investigated. Findings

show that Motowo and Church Street have very small vulnerability to floods, Cassava Farm and Clerks Quarters have high

vulnerability to floods and Down Beach with an index of 0.84 has a very high vulnerable to floods. From the results coastal

communities are significantly different (p < 0.01) in terms of vulnerability to flood hazards. A total of 19.39% of the population

highlighted that the adaptation strategies put in place to help combat floods in their neighborhoods are effective while 80.61% of

the respondents decried that the measures were not effective. Coping strategies need to take into consideration the myriad of

factors involved in the determination of vulnerability so as to help putting in place a comprehensive multi-risk adaptation strategy.

Policies implications of the results warrant a conscious effort by the council to clear chocked gutters, culverts and major drains to

ease water flow especially during the rainy seasons and local authorities and ministries must make sure proper land use plans are

in place and are enforced without any fear or favor so as to ensure resilience to flood risks.

Keywords: Adaptation, Coastal Areas, Exposure, Flood Risk Assessment, Flood Vulnerability Index Introduction

1. Introduction

Vulnerability assessments are nowadays considered as key

issues towards effective flood disaster risk reduction [1]

because in many vulnerable countries, disaster management is

mainly concentrated on emergency response, disaster relief,

and rehabilitation activities at the expense of site specific

socio-economic and environmental conditions. Several

studies therefore suggest a paradigm shift from disaster relief

and response to disaster risk and vulnerability reduction [1, 2].

It is therefore imperative to develop vulnerability indicators

which will enable decision-makers to assess the impact of

disasters [3] and put in place sustainable measures because of

the close relationship between sustainable development and

vulnerability assessment. The three main pillars of sustainable

development are: social, economic, and environmental [4, 5].

25 Usongo Patience Ajonina et al.: Assessing Flood Vulnerability Index for Policy Implications Towards Flood Risk Management Along the Atlantic Coast of Limbe, Cameroon

It is evident that social, economic and environmental

conditions exist within a society that result to some groups of

people living with an amplified state of vulnerability [6].

Assessing vulnerability using the socio-economic and

physical dimensions aid in the identification of the most

vulnerable regions together with key factors which, once

addressed, could increase the resilience of local communities.

Flooding is a major challenge of many coastal cities across

the world due to their lower geographic elevations and higher

population densities than that of inland communities [7].

Coastal floods are regarded as among the most dangerous and

harmful of natural disasters [8] posing serious threats to the

environment and development. Satterthwaite [9] found out

that many coastal cities lack good infrastructures such as

drainage, water storage, sanitation, roads, healthcare and

emergency services system. These conditions increase the

vulnerability of populations to different forms of hazards [10].

The frequency of flood disasters has amplified throughout the

world with the number of disasters and affected people having

doubled during 1990 to 2000 [11]. A major contributing factor

is climate change which has made human and natural systems

more vulnerable to disasters due to its impacts on these

systems [12, 13]. Developing countries are at risk of climate

change, and therefore most vulnerable to flood disasters [14],

mainly due to lack of resources to adapt (socially,

technologically and financially) to such disasters [15, 16], the

high concentration of economic and industrial development

along the coast [17], growing population in the coastal zones

[18], low adaptive capacity due to persistent poverty and poor

planning [19] and a lack of knowledge on site specific

vulnerabilities which have often resulted in not putting in

place appropriate measures.

Of all-natural disaster deaths, 97% occur in developing

countries. Asia and Africa are the most affected continents

with floods accounting for half of these disasters. Much of

Africa is vulnerable to flooding with episodes of floods

accounting for 26% of total disaster occurrences (Institute for

Catastrophic Loss Reduction [20]. Floods contribute

extensively to loss of life, damage of property, and promote

spread of diseases such as malaria, dengue fever and cholera

over coastal communities in Africa [21]. From 1900 to 2006,

floods in cities of Africa killed nearly 20,000 people and

affected approximately 40 million people. Total cost of

damage was estimated at about US$4 billion [22]. Increased

flood incidence in African cities has affected poor households

and businesses in densely populated areas and economically

important coastal communities [23]. Hence, understanding the

extent of flood vulnerability and the capacity of local

communities to adapt to the impacts should be a call for

concern for all stakeholders as a precursor for promoting

sustainable management of floods in an era of global climate

change. Since global climate change is probably going to

accentuate coastal flooding and also exacerbate many other

problems already confronting coastal communities in Africa

[24], there is a dire need for a regular assessment of

vulnerability and coping strategies adopted in order to help

community planning and emergency management.

Vulnerability measurement is vital for community planning

and emergency management. Vulnerability, often expressed as

an index, has been conceptualized in a myriad of ways. A

number of studies have demonstrated various approaches for

coastal vulnerability assessment [25-27]. The vulnerability of

a community to a flood hazard is often measured using

socioeconomic indicators or calculating physical flood extents.

However, their combined impact is usually ignored.

Cameroon like many other developing countries still suffer

devastating effects of floods. Limbe a coastal town has

recorded devastating floods over the years and factors

accounting for the perennial floods in Limbe remain both

natural and human [28]. Flooding has greatly impacted the

town by enormously damaging property and infrastructure,

resulting to substantial loss of lives and livelihoods of

community members. The floods of June 2001 and July 2014

affected about 3000 inhabitants, notably, within the

neighborhoods of Mabeta, New layout, Mile 2, Towe, Clerks

Quarters, Livanda, Congo Moyo, Motowo, Mbonjo and

Mbende [28]. There was the loss of 23 human lives, about

50persons were injured, 78 houses were completely destroyed,

community infrastructure (roads, water and electricity supply

lines, communication systems, schools, hospitals, churches,

etc.) were disrupted and the environment was polluted and

degraded. Indirect losses include: unemployment, disruption

of economic activities and livelihoods. All loses were

estimated at CFA 1.5 billion Frs. ($3 million U. S) [28].

Although disaster management plans at local, regional and

national levels are available and are regularly updated

annually, there are still not able to manage floods and reduce

damages and community vulnerabilities. The few studies so

far carried out in the area are those characterizing the

outcomes of flood hazards and how vulnerable populations

perceive and adapt to flood risks [29, 30]. To fill the gap

additional research is imperative to examine the perceived

causes of floods and provide a practical identification and

assessment of socio-economic and physical vulnerabilities in

the area by assigning measurable numeric indicators to serve

as a good reference and a scientific foundation to local

authorities in flood disaster mitigation as well as well as aid in

generating a sound flood mitigation program for Limbe. This

study was aimed at calculating the FVI and to proposes policy

implications for flood risk management along the Atlantic

coast. It sets out to answer the following research questions: (1)

What are the perceived causes of flood disaster? (2) What are

the adaptation measures put in place and how effective are

these measures? (3) How have the socio-economic dimension

of susceptibility, socio-economic indicators of resilience and

the physical indicators of exposure influence the FVI in the

area? (4) What policies need to be proposed in order to ensure

the sustainable management of floods?

2. Materials and Methods

2.1. Study Area

Limbe is located between Latitude 4°.02 north of the

American Journal of Water Science and Engineering 2021; 7(2): 24-38 26

equator and longitude 9°.21 east of the Greenwich Meridian

and it is situated at an elevation of 69 meters above sea level. It

is a natural coastal and exotic city nestled between the

Cameroon Mountain (Mount Fako) and the Atlantic Ocean. It

is the link between the ocean and the continental segment of

the Cameroon Volcanic Line (CVL) and is found where the

Cameroon Mountain extends into the Gulf of Guinea. Limbe

has a total surface area of 549km2 and its coastline is

dominated by volcanic rocks that stretch from Debundscha

(the second wettest place in the world) to Man O’War Bay.

Figure 1 shows the location and sample sites within Limbe 1

municipality in Fako Division, South West Region. Limbe I

council area has an equatorial climate with annual average

temperature of 27°C and an annual average rainfall of above

500mm. The annual temperature range is 21.45°C to 32.75°C.

The rainfall varies from 114.0mm to 1053.0mm per month.

Peak rainfall is experienced during the rainy season which

lasts from mid-June to October. The dry season records the

lowest amount of rainfall which lasts from November to April

ending.

The city is a low-lying coastal plain with the highest points

reaching 362 m above sea level [31]. Within the town of Limbe,

small streams flow into larger drainage beds that converge into

two main rivers (Limbe and Jenguele) that empty into the

Atlantic Ocean. These rivers frequently overflow their banks in

the rainy season causing floods in the low-lying areas that are

just 1–2m above sea level. The water table is highly stable and

high in areas like Clerks Quarters thereby impeding infiltration

following rainfall making the area vulnerable to floods. Figure

2 shows the drainage map of Limbe 1.

The population of Limbe is estimated at 120000 inhabitants

according to the 2007 census. A drive through the city

indicates an acute spatial disorganized view of settlements

with a mixture of planned and unplanned settlements. The city

generates so much garbage daily, of which the council is

unable to collect due to their existing capacity and facilities. It

is very common to find garbage poorly disposed in drains,

gutters, along river banks and illegal dumpsites. Following

heavy rainfall most of the waste is carried by runoff blocking

gutters and drains and increasing the risk of flooding

Figure 1. The location map of Limbe 1 municipality.


Figure 2. Drainage map of Limbe 1.

Source: Generated from Aster DEM 2018.

Figure 3. Slope map of Limbe showing the sample sites.


2.2. Sampling Procedure and Sample Sites Selection

A multi-stage random sampling design with a two-stage

sampling was used for the study. The first stage was the

selection of the sampled sites. It involved communities

located along the Atlantic coast. A stratified random sampling

design was used in selecting the sampling sites. To achieve

this, the map of the area was super imposed on a digital

elevation model (DEM) image of the area from where a slope

map was generated with values ranging 0-2° to above 35°

(Figure 3). Five sample sites with slopes values of 0-2°

indicating a high vulnerability to flood hazard were randomly

selected for the study. The communities selected were:

Cassava Farms, Clerks Quarters, Church Street, Motowoh and

Down Beach communities.

The second stage involved the selection of the sample

population for vulnerability assessment and adaptation

strategies put in place. A systematic random sampling design

was used in selecting the households for interviews and

questionnaires administration. It involved selecting a sample

unit at random, then selecting every nth unit systematically.

The nth unit varied depending on the number of households.

In large communities, a step of 5 houses was chosen while in

small communities there was a step of 3 houses. Within the 5

communities, household heads were chosen.

2.3. Flood Vulnerability Indicators Selection

Vulnerability is ‘the characteristic of a community’s system

that make it susceptible to the damaging impacts of a hazard’

according to the United Nations International Strategy for

Disaster Reduction [32]. Vulnerability is determined by the

physical, social, economic, and environmental factors, which

can increase susceptibility to the impact of hazards’ [33, 34].

The United Nations Development Programme (UNDP)

defines vulnerability as ‘a human condition or process

resulting from physical, social, economic and environmental

factors, which determine the likelihood and scale of damage

from the impact of a given hazard’ [35]. Assessing

vulnerability involved analyzing the elements of exposure,

susceptibility and resilience of any system to a hazard [7].

Exposure and susceptibility are considered as the stressors of the

system which increase vulnerability while resilience is considered

as the potential of the system to reduce the impact of the hazard.

Exposure is defined as ‘the degree, duration and/or extent to which

a system is in contact with, or subject to, perturbation’ [36, 37].

Most environments or communities are exposed to hazards by

virtue of their location. Susceptibility reflects the capacity of

individuals, groups, or physical or socio-economic systems to

withstand the impact of the hazard. Susceptibility s defined as the

elements exposed within the system, which influence the

probabilities of being harmed during floods. The susceptibility

indicators embrace general information on social relations, climate

and population with special needs (children, elderly or disabled)

among others. Resilience is the success of adaptation measures

despite challenging circumstances. Resilient indicators are those

that can help give us a clue on the success of the coping measures

put in place, the way the population perceive floods and presence

of structures and measures that can aid the community to adjust

and cope with the impacts of flood hazards.

The selection of appropriate indicators for the study was

done using a deductive approach based on existing principles

and the concepts of vulnerability. A total of 22 variables were

selected, representing the most relevant factors shown to

influence vulnerability to flood hazards and were grouped into

susceptibility indicators (15), exposure (2) and resilience (5)

as shown on Figure 4.

Figure 4. Vulnerability assessment indicators for the study.


2.4. Data Collection Methods

The study made use of primary and secondary data sources.

Primary data was obtained using a hand held Global

Positioning System (GPS) to take the elevation and distance

from the coast to selected neighborhoods. Socioeconomic

indicators of susceptibility and resilience were collected

through questionnaire-based interviews at household levels

and personal observation. Secondary data was obtained from

various sources such as journal articles and other documented

sources. Maps were also generated from the United States

Geologic Survey (USGS), Aster DEM data base via ArcGIS

and QGIS software. Some secondary information was also

gotten from the council pertaining to the details of the study

area.

2.5. Vulnerability Assessment

Societies are vulnerable to floods due to three main factors:

exposure, susceptibility and resilience [38]. This study made

use of the system approach to identify the interactions of

different actors or components within the coastal system [7].

In this study, the vulnerable people in a community were

expressed in two closely linked ways: (1) socio-economic

vulnerability (susceptibility) and (2) physical vulnerability

(flood exposure) The vulnerability assessment method used

was the vulnerability index system. Defined indicators related

to susceptibility and exposure were measured.

The procedure for calculating the Flood Vulnerability Index

(FVI) starts by converting each identified indicator into a

normalized value (on a scale from 0 to 1), dimensionless

number using predefined minimum and maximum values

from the spatial elements under consideration. Standardization

of indicators’ values is expected to create a comparative

proportion among the indicators.

The formula used for normalization was:

VI=(Xij – Min Xi) / (MaxXi – MinXi) (1)

where

Vij=the standardized vulnerability index for a vulnerability

parameter i for a vulnerable place j

Xij=the observed value of a particular place j for a particular

vulnerability parameter i

Min Xi and Max Xi=the minimum and maximum values of

the observed range of values of a vulnerability parameter

Normalized indicators were subsequently used for

vulnerability indices calculations. The FVI of each coastal

component (socio-economic and physical) was computed

based on the general flood vulnerability index (FVI) formula

(Eq. 2).

FVI=E*S/R (2)

The general formula for FVI was computed by categorizing

the indicators of the factors of exposure (E), susceptibility (S)

and resilience (R) according to [39].

With regards to coastal vulnerability indicators equation (2)

was transformed to represent the different vulnerability

dimensions to be used in the study. An aggregate value of

socioeconomic vulnerability of various neighborhoods was

calculated as the average of standardized indicators values

using the following equation according to [40]

Socioeconomic Vulnerability (susceptibility) =��

� (3)

Where SSEI1=Standardized socio-economic indicator 1

SSEIX=Standardized socio-economic indicator X

X=maximum number of indicators

Physical vulnerability was calculated using the normalized

ranked values of altitude and distance from the coast using the

following equation.

Physical vulnerability (exposure) =�"�� "�� "�#

# (4)

Where SPI1=Standardized physical indicator 1

SPI2=Standardized physical indicator 2

SPIY=Standardized physical indicator Y

Y=maximum number of indicators

Resilience was also calculated using the normalized values

of resilience indicators with the following equation.

Resilience =�%�� %�� %�&

& (5)

Where SRI1=Standardized resilience indicator 1

SRI2=Standardized resilience indicator 2

SRIZ=Standardized physical indicator Z

Z=maximum number of indicators

The total FVI of each sample site is the average of four FVI

(Eq 3-5) and the result was interpreted following [7] flood

vulnerability interpretation as seen on Table 1 below. The

index is expressed on a scale from 0-1 signifying low or high

flood vulnerability and shows which areas need detailed

investigation for selecting more effective measures and

appropriate strategies to be undertaken.

Table 1. Flood vulnerability interpretation scale (after [7]).

Index value Description

<0.01 Very small vulnerability to floods

0.01-0.25 Small vulnerability to floods

0.25-0.50 Vulnerable to floods

0.50-0.75 High vulnerability to floods

0.75-1 Very high vulnerability to floods

2.6. Statistical Analysis

The analysis of variance was used to test the vulnerability

within the neighborhoods. Results of the FVI for the different

neighborhoods were subjected to statistical analysis. One-way

ANOVA using the SPSS version 18.0 package was performed


to investigate whether significant variations in vulnerability

do occur over space in the area.

3. Results and Discussions

3.1. Perceived Causes of Flood Disaster

People tend to adjust to natural hazards based on what they

think are the causes. The study revealed that the causes of

floods in Limbe are multifaceted as shown on Figure 5. Floods

in Limbe have been linked to several causes

A majority of the respondents (78%) identified high rainfall

as the main cause of flooding in Limbe. Another finding was

that temperature has increased over time. The changes in the

climate based on temperature, supports the position of [41]

that global temperature will increase by 6.4% by the end of

this century with sea level rising at a rate of 59cm. Several

studies have also linked flood events to climatic factors [42].

This also tallies with the findings of [43] who stated that

floods are natural phenomenon aggravated by extreme climate

change and hydro-meteorological events.

Apart from the physical problem of climate, respondents

also identified human induced causes as catalysts to the extent

of damage on properties and loss of lives. A total of 30.6%

respondents mentioned wastes thrown in drains as the major

human induced cause of floods in Limbe. Indiscriminate

disposal of refuse into streams and storm drains, construction

of undersized drains and culverts often blocked the free flow

of runoff resulting to floods. This is in line with the work of

[44, 45] who said that, although about 60 to 75% of solid

waste generated in the city is collected, the solid waste that

remains uncollected often finds its way into open drains, thus

obstructing the free flow of water causing overflows that

result in floods.

Figure 5. Household perception on the causes of flood in Limbe.

Other human induced causes were settlement expansion

(24.0%), Gods anger (8.7%), witchcraft (5.5%), clearance of

forest lands (16.9%) and building within flood plains.

Population increase and pressure in the area has pushed many

to construct houses on marginal lands. According to [9],

hundreds of millions of urban dwellers live-in poor-quality

homes on illegally acquired and marginal lands. This reduces

the desires of individuals to invest in more resilient building

structures and areas. As a result, many wooden structures are

found in these areas. The increasing practice of building on

watercourses and wetlands, indiscriminate dumping and

silting of drains has exacerbated the perennial urban floods in

Limbe. This result confirms the findings of [46, 47] who

highlighted that increase incidence of floods is because of the

low-lying nature of the land, high rainfall intensity and

duration, deposition of sediments in storm drains

3.2. Socio-economic Dimension of Susceptibility

Table 2 below presents the results of the 15 socio-economic

susceptibility indicators measured.

Field results show that 31.4% of households in Down Beach

are not aware of flood risk zones as compared to only 14.6% in

Cassava Farm. Poor knowledge of flood risk zones can result

in the construction of houses in zones that are prone to floods.

With the exception of Clerks Quarters where only 21.6% of

the population have no cars, all the other neighborhoods have

more than 60% of their population with no cars thereby

hindering fast evacuation whenever there are floods. The high

proportion with no car ownership is because of the high rate of

poverty within the neighborhoods. The findings of [48]

supports this trend that vulnerability results from poverty.


Table 2. Socio-economic indicators of susceptibility showing the percentage of households affected.

Susceptibility Indicators

Sample Sites showing % households

Neighborhood

Cassava Farm Church Street Clerks Quarters Down Beach Motowo

Aware of flood risk zones 14.6 19.4 19.4 31.4 14.7

Car ownership 75.6 80.6 21.6 62.9 70.6

Community Awareness 7.3 2.8 8.1 11.4 2.9

Disable people 7.3 22.2 18.9 14.2 26.5

Education: no schooling 7.3 0 8.1 8.5 11.8

Farmers / fishermen 14.6 8.3 8.3 8.3 14.7

Household size (more than 10) 12.5 3.4 3.8 7.1 12.5

Income below 100,000frs 58.5 22.2 32 22.9 32.4

Land ownership 7.3 22.2 18.9 22.9 47.1

Locally made toilets 14.6 8.3 10.8 25.7 32.4

No access to climatic information 78.1 77.8 81.1 74.3 73.5

No access to media information 7.3 25 10.8 8.6 17.7

No access to road 12.2 13.9 21.6 11.4 26.5

No waste collection cans 31.7 19.4 18.9 40 20.6

Poor building material 17.4 19.4 27 20 29.4

With regards to community awareness of floods, results

revealed that with the exception of Down Beach with 11.2% of

households not aware of floods, the other neighborhoods have

less than 8% of their population not having a knowledge of

floods. Knowledge of flood awareness is important because

awareness is a necessary precursor to preparedness.

Results from the field further show that the percentage of

households with disable persons in Motowo was 24.5% and

22.2% in Church Street making then the most susceptible

neighborhoods than the other neighborhoods of the study. This

is because in times of floods those who are not disabled can

easily escape from the hazard than those who are disabled

there by increasing the mortality rates in these neighborhoods

in times of flooding. This corrobates with the work of [26]

who states that the disabled are particularly vulnerable.

Percentage household heads that have not been to school

were 7.3%, 8.1%, 8.5% and 11.5% respectively for Cassava

Farm, Clerks Quarter, Down Beach and Motowo. The

inability to read and understand disaster communication

makes many communities to be more vulnerable to floods.

Field results revealed that Cassava Farm and Motowo have

14.6% and 14.7% respectively farmers’/fishermen household

compared to other neighborhoods with just 8.3%. Where

household heads are predominantly made up of

farmers/fishermen the neighborhood is bound to be more

susceptible to floods compared to those that are predominantly

dominated by civil servants. This is because the level of

exposure, reasoning faculty, income levels and the manner in

which they perceive and cope with floods are different.

Cassava Farm and Motowo have 12.5% of households having

more than ten persons while Clerk Quarters and Church Street

have less than 4% of household sizes of more than ten persons

indicating a lower susceptibility to floods. From the results

58.5% of household heads within Cassava Farm have income

levels below 100,000frs while the other neighborhoods have

less than 33% household heads within this income level.

thereby making them to be more susceptible to flood as they

will turn to occupy the most hazardous geographical areas and

most poorly maintained buildings, which result in the greatest

physical impacts such as casualties and property loss during a

disaster This relates to the findings of [49], who stated that;

poverty is a major factor that increases vulnerability and

impacts of floods.

Percentage tenant households recorded by the study was 47.1%

in Motowo, 22.9% in Down Beach, 22.2 in Church Street, 18.9%

in Clerks Quarters and 7.3% in Cassava Farm. Land tenurership

can strongly influence the level of control a resident has over the

adoption of protective measures leading to differences in flood

susceptibility among owners and renters.

Results revealed that 32.4% households in Motowo, 25.7%

in Down Beach, 14.6% in Cassava Farm, 10.8% in Clerks

Quarters and 8.3% in Church Street are using locally made

toilets (Table 2). Neighborhoods with such toilets are highly

affected during floods exposing the population to health risks

through the contamination of water sources resulting to a high

prevalence of waterborne diseases. The findings of [50]

support the trend that most urban poor dwellers are

increasingly exposed to hazards due to the poor conditions

they are subject to.

Access to climatic and media information is important in

creating awareness on the probability of a flood occurring

thereby enabling preparedness. More than 70% of households

in all the neighborhoods attest to the fact that they do not have

access to climatic data nor information on early warning

signals of flood occurrences.

With the exception of Church Street neighborhood where

25% of households attest to not having access to media

information, less than 18% households do not have access to

media information in the other neighborhoods. The percentage

of households with no access roads was 12.2%, 13.9%, 21.6%,

11.4% and 26.5% respectively for Cassava Farm, Church

Street, Clerks Quarters, Down Beach and Motowo. Areas with

no access roads will hinder movements of persons and

property resulting to heavy casualties and damages.

The percentage of household with no access to waste

collection bins is 31.7%, 20.6% 19.4%, 18.9% and 40%

respectively for Cassava Farm, Motowo, Church Street,

Clerks Quatres and Down Beach. This indicates that Down


Beach and Cassava Farm neighborhoods are more susceptible

to floods that can result due to poor waste disposal than the

other neighborhoods.

A total of 29.4% households in Motowo, 27% in Clerks

Quarter, 20% in Down Beach, 19.4% in Church Street and

17.4% in Cassava Farm are living in poorly constructed

houses that cannot resist the force of flood waters.

3.3. Socio-economic Indicators of Resilience

The analysis of the five resilience indicators are presented

on Table 3.

Table 3. Socio-economic indicators of resilience showing percentage of households.

Resilience indicators Sample sites showing % of household

Cassava Farm Church Street Clerks Quarters Down Beach Motowo

Perception of flood severity 37.1 55.9 46.3 56.8 47.2

Success of flood control measures 21.6 24 14.6 24 17

Long term resident 15 years 11.4 23.5 22 21.6 33.3

Access to hospital 90.2 63.9 78.4 74.3 52.9

Community group membership 24.4 44.4 29.7 25.7 35.3

Results revealed that 55.9% of the population in Church

Street agreed to the fact that floods in the past were more

severe than current floods. The persons that agreed to this

statement were 56.8% in Down Beach, 46.3% in Clerks

Quarters, 47.2% in Motowo and 37.1% in Cassava Farm

(Table 3). Floods are believed to reduce following the putting

in place better coping measures to reduce their impacts.

Within the Down Beach and Church Street neighborhoods, 24%

of the households attested to the success of flood control

measures, 21.6% in Cassava Farm, 17% in Motowo and 14.6%

in Clerks Quarters. The percentage of the total sampled

population with long term resident of over 15years as revealed

by the study is as follows: 33.3% in Motowo, 23.5% in Church

Street, 22% in Clerks Quarters, 21.6% in Down Beach and

11.4% in Cassava Farm. Longer duration of stay is associated

with greater awareness, understanding and personal action. It

is believed that longevity in an area not only gives more

experience but the buildup of adaptive measures over the

years which go a long way to guarantee resilience. The

percentage of the population having access to hospital in the

study area is 90.2% in Cassava Farm, 76.4% in Clerks

Quarters, 74.3% in Down Beach, 63.9% in Church Street and

52.9% in Motowo. Casualties from injuries and illnesses

following a flood event will drastically be reduced where there

is access to medical facilities. Results indicated that 44.4%

and 35.5% of the population of Church Street and Motowo

respectively are members of a community group while the

representation in the other neighborhoods is less than 30%.

Community group membership is an important aspect of

community mobilization targeted at carrying out activities

such as cleaning of drains and gutters to facilitate the free flow

of flood waters, building of sand bags to prevent the

encroachment of flood water among others.

3.4. Physical Indicators of Exposure

Two indicators selected to measure exposure were elevation

and distance from the coast as seen on Table 4 below.

Table 4. Physical indicators of exposure.

Indicator Neighborhoods

Cassava Farm Church Street Clerks Quarters Down Beach Motowo Mean

Distance from the coast (m) 204 586 436 173 614 403.26

Elevation (m) 18 14 5.3 7 17 12,3

Results obtained from the field show that the

neighborhoods are variably exposed to flood hazards in Limbe.

Down Beach which is situated at 173m from the Atlantic coast

is more exposed while Motowo with a distance of 614m from

the coast is the least exposed. Field results obtained revealed

that the height above sea level ranges from 5.3m in Clerks

Quarters to 18m in Cassava Farm showing that Clerks

Quarters is the most exposed neighborhood and Cassava Farm

the least exposed.

3.5. Vulnerability Assessment

The susceptibility, exposure and resilience indices were

calculated for all the neighborhoods and the results can be

seen on Table 5.

The susceptibility index ranges from 0.29 in Cassava Farm

to 0.65 in Motowo (Table 5). Motowo is highly susceptible to

floods compared to other neighborhoods. This is not

surprising since in 8 out of the 15 indicators under

susceptibility (Table 2), Motowo recorded the highest

percentage in number of disable people (26.5%), persons with

no formal education (11.8%), farmers / fishermen households

(14.7%), household size (more than 10 persons) (12.5%),

locally made toilets (32.4.7%), no access to media information

(17.7%), no access to road (26.5%) and poor building material

(29.4%) compared to other neighborhoods.

The index of exposure ranged from 0.88 in Down Beach to

0.13 in Motowo. Down Beach is the most exposed

neighborhood to floods. Locational factor of distance to the

sea is highly responsible for this. The distance to sea (coastline)

is 173m which is the closest compared to other neighborhoods.

Clerks Quarters is exposed by virtue of its lowest height above

sea level which averagely is 5.3m.

The resilience index ranged from 0.3 in Cassava Farm to

0,52 in Church Street. Church Street is the most resilient


neighborhood and some of the factors that have contributed to

this include; secured buildings with only (17.4%) of houses

constructed with poor building materials, high access to

hospital facilities (90..2%) and low perception of flood

severity (37.1%) a higher elevation of 18m above sea level

compared to other neighborhoods. Vulnerability to flood

according to [51] varies due to socio-economic status,

available knowledge on flooding and attitudes of people

towards environmental management

Table 5. Susceptibility, exposure and resilience indices.

Factor Sample sites

Cassava Farm Church Street Clerks Quarters Down Beach Motowo Mean value

Susceptibility 0.44 0.29 0.37 0.45 0.65 0.44

Exposure 0.38 0.38 0.75 0.88 0.13 0.5

Resilience 0.3 0.52 0.39 0.47 0.45 0.43

The total vulnerability index was calculated and the results

are presented on Figure 6. Based on the flood vulnerability

interpretation of [7] Motowo and Church Street have very

small vulnerability to floods, Cassava Farm and Clerks

Quarters have high vulnerabilities to floods and Down Beach

with an index of 0.84 has a very high vulnerable to floods. The

high vulnerability of Clerks Quarters and the very high

vulnerability of Down Beach are due to locational or physical

factors such as average distance to the sea and elevation. They

are very close to the sea and have lower elevations.

Figure 6. Flood vulnerability index.

The results obtained were subjected to ANOVA to see

whether there exists a significant difference in vulnerability to

floods within the neighborhoods. The results show that coastal

communities are significantly different (p < 0.01, F=26.700,

df=2) in terms vulnerability to flood hazards in Limbe. In

other words, there are neighborhoods that are more vulnerable

than others. This is in line with the work of [51] who also

reported variations in flood vulnerability in Vietnam.

This study has presented a comprehensive baseline

understanding of location-specific vulnerability assessment to

flood risk along the coast of Limbe, where despite frequent

occurrences of floods, data on which specific population

groups are at a higher risk are lacking. By providing the

relative vulnerability of different communities under

relatively similar climate conditions, the study has brought to

light a critical understanding of the key factors that drive

location-specific vulnerability. Therefore, this can form the

basis for developing context-specific adaptation and

mitigation options to flood vulnerability.

3.6. Adaptation Strategies to Flood Disaster

Urban areas are not prone to disaster by nature alone; but

also due to other factors such as the socio-economic

conditions and processes, rapid urbanization and migration

which also increase the risk of urban dwellers to flood

disasters. Migrants, settle in areas that originally are liable to

floods with pre-existing weak structural conditions with

severe consequences. The adaptation measures identified in

the study area together with the percentage of those adopting

were; relocation (63.95), the building of sandbags (66.7%),

construction of embankments (69.9%), building of flood

defense walls (63.9%), joining community group works

(53%), construction of retainer walls (66.1%) and having a

house flood plan (76.5%) as shown on Table 6. These

strategies were identified not to be effective in the study area.

It was noticed that only a small proportion (19.39%) of the

population highlighted that the adaptation strategies put in

place to help combat floods in their neighborhoods are

effective while 80.61% of the respondents decried that the


measures were not effective. However, mitigation measures so

far put in place are not very effective because they have failed

to address generally the diverse causes of floods and also to

take into consideration location-specific peculiarities of flood

vulnerability resulting to severe impacts of floods in the area.

Table 6. Adaptation Strategies to Flood Incidences in Limbe.

Current Adaptation Strategies to Flood Incidences in Limbe

Parameters Relocation

Building of

sand bags

around

water entry

points

Construct

embankment

around your

nearby

stream

Construction

of retainer wall

Construction

of flood

defense wall

Community

group work

Tree

Planting

Raise your

well/borehol

e above flood

levels

Operate a

House flood

plan

% Adopted 35.5 33.3 30.1 33.9 36.1 47 26.8 20.8 23.5

% not

Adopted 63.95 66.7 69.9 66.1 63.9 53 73.2 79.2 76.5

Mean 1.65 1.67 1.7 1.66 1.64 1.53 1.73 1.79 1.77

Median 2 2 2 2 2 2 2 2 2

Mode 2 2 2 2 2 2 2 2 2

Std. Dev 0.49 0.473 0.46 0.475 0.482 0.501 0.444 0.407 0.425

Variance 0.24 0.223 0.211 0.225 0.232 0.251 0.197 0.165 0.181

Range 2 1 1 1 1 1 1 1 1

Minimum 1 1 1 1 1 1 1 1 1

Maximum 3 2 2 2 2 2 2 2 2

Sum 302 305 311 304 300 278 317 328 323

Table 7. Household Perceptions on the Implications of flood in Limbe.

Household Perception of the Impacts of flood in Limbe

Parameters Destruction

of Buildings

Crop

Destruction

Prevalence of

Water Borne

Diseases

Death Displacement

of Households

Damage to

household

property

Favor Crop

Growth

Pollution

of well

water

Road

Destruction

% Strongly Agree 38.8 26.8 35.5 12.6 30.1 43.7 10.9 30.6 52.5

% Agree 20.2 25.7 23.5 13.1 19.1 21.3 8.7 22.4 27.3

% Disagree 30.1 31.7 30.1 46.4 36.6 23.5 53 31.7 13.7

% Strongly Disagree 10.9 15.8 10.9 27.8 14.2 11.5 27.3 15.3 6.5

Mean1 2.13a 2.37a 2.16a 2.90b 2.35a 2.03a 2.97b 2.32a 1.73a

Median 2 2 2 3 3 2 3 2 1

Mode 1 3 1 3 3 1 3 3 1

Std. Deviation 1.056 1.044 1.035 0.961 1.058 1.066 0.895 1.068 0.916

Variance 1.115 1.09 1.072 0.924 1.119 1.137 0.801 1.141 0.839

Range 3 3 3 4 3 3 3 3 3

Minimum 1 1 1 1 1 1 1 1 1

Maximum 4 4 4 5 4 4 4 4 4

Sum 390 433 396 531 430 371 543 424 315

NB: N=183; 1 Overall Mean Decision for 4-point Likert scale questions is 2.5 (Mean value of below 2.5 is a tendency towards agreement (a Significant factor)

while a mean score of above 2.5 to 4 is a tendency towards disagreement (b Not significant factor)

Figure 7. Evidence of flooding impacts in Limbe, 2018 (A) Major public access road affected by flood water (B) Residential houses submerged by flooding.


Figure 8. Ranked perceived impacts of floods based on the % of households that agreed.

3.7. Impacts of Flood Disaster

With regards to the findings, flooding is the most

predominant disaster in Limbe despite the adaptation

strategies so far put in place. Flooding is most frequent in

Limbe and the consequences have been grievous. Each year,

the months of June to August are often characterized by heavy

torrential rains causing serious distress to the local population

and great embarrassment to the municipal authorities and local

government officials. Nine impacts were identified by the

researcher and they include; destruction of buildings,

destruction of crops, prevalence of water borne diseases, loss

of lives, displacement of households, damages to household

property, increase crop growth, pollution of well water and

destruction of roads. Using a four-point Likert scale analysis, a

mean value of 2.5 (1+2+3+4 divided by 4) was obtained. This

is a weighted mean score from which the decision on

household perception on the impact of flood was drawn. As

such, a weighted value of below 2.5 indicates that the factor is

significant while above 2.5 indicates that it is not significant.

However, from Table 7 it can be seen that respondents

perceived destruction of roads with a mean value of 1.73 as the

most significant impact (Figure 7) followed by the damages of

household property with a mean value of 2.03, destruction of

buildings with a mean of 2.13, prevalence of water borne

diseases with a mean of 2.16, pollution of well water 2.32,

displacement of household 2.35 and destruction of crops 2.37.

While the non-significant impacts include death occurrences

(2.90) and impact on crop growth (2.97). Figure 8 shows the

ranked perceived impacts of floods based on the percentage of

households that agreed. Floods are the most disastrous,

frequent and widespread disaster causing extensive damage to

lives and property. This therefore affirms the views of [43] that

floods are by far the most hazardous, disastrous, frequent and

widespread disaster throughout the world causing extensive

damages to lives and property especially in developing

countries where there are still bottle necks in achieving

sustainable resilience.

4. Conclusion and Policy Implications

The Limbe municipality has suffered from devastating

floods throughout the years and the residents are increasingly

vulnerable to flood disasters. With flooding incidences

unevenly distributed depending on one’s location, the

complexities of the urban environment and urbanization and

social problems is evident. Slums and low residential areas are

noted to be the most vulnerable to floods. In spite of the

literature supporting the view that floods in Limbe are

attributable to changes in climatic conditions (rainfall

intensity), human factors have intensified the situation.

Frequency of flood occurrence, fatality of disaster and the

extent of property damage were noted to vary among

communities. The study recognized the need to sensitize and

create awareness on environmental management, reinforce

city planning and management, development control and

enforcement. The Limbe municipality especially the study

sites remain vulnerable to floods and this is a major urban

challenge. Thus, there is a need to put in place urgent and

robust measures targeted at reducing vulnerabilities and

building a resilient city taking into consideration the peculiar

vulnerabilities of various neighborhoods which has largely

been ignored in the past.

1. There must be conscious efforts to clear chocked gutters,

culverts and major drains in the city to ease water flows

especially during the rainy seasons. Households should

be encouraged to adopt best waste management practices

through provision of dustbins and timely collection of

refuse.

2. Land use planning, implementation and enforcement are

very necessary in the current flood disaster challenges.

With a good land use plan, areas uninhabitable are well


marked out and a proper drainage system can be

developed. City authorities and ministries must make

sure proper plans are in place and enforced without any

fear or favor. The Town and Country Planning

Departments must provide and strengthen the necessary

actions towards granting development permits,

controlling building and construction codes and ensuring

that they are strictly followed. This will go a long way to

regulate building on waterways and development of

slum settlements.

3. The council of Limbe should also ensure that there is a

warning signal against the construction of houses in

flood risk areas. No housing permits should be given to

the occupants of these areas.

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Biography

Usongo Patience Ajonina is a senior lecturer and researcher in the Department of Geography, University of Buea, Cameroon. She has

published several articles in peer reviewed academic journals internationally.

Tepoule Nguéke Joseph is a lecturer and researcher in the Department of Geography, University of Buea, Cameroon. He has published

several articles in peer reviewed academic journals internationally.

Chang Linda Meh is a post graduate student in the department of Geography University of Buea, Cameroon.

assessing flood vulnerability index for policy

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